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\section*{Introduction}  For biologists interested in understanding the relationship between fitness, genotype, and phenotype, modern sequencing technologies provide for an unprecedented opportunity to gain a deep understanding of genome level processes that together, underlie adaptation. Transcriptome sequencing has been particularly influential, and has resulted in discoveries not possible even just a few years ago. This in large part is due to the scale at which these studies may be conducted. Unlike studies of adaptation based on one or a small number of candidate genes (e.g. \citep{Fitzpatrick:2005vd,Panhuis:2006kp}), transctiptome studies may assay the entire suite of expressed transcripts -- the transcriptome -- simultaneously. In addition to issues of scale, newer sequencing studies have much more power to detect lowly expressed transcripts, or small differences in gene expression as a result of enhanced dynamic range. range \citep{Wolf:2013hd,Vijay:2012gy}.  and as a direct result, a diverse toolset for the assembly and analysis of transcriptome exists. Notable amongst the wide array of tools include several for quality visualization (FastQC (\url{http://www.bioinformatics.babraham.ac.uk/projects/fastqc/}) and SolexaQA \citep{Cox:2010ch}) read trimming (e.g. Trimmomatic \citep{Bolger:2014ek} and Cutadapt \citep{Martin:2011va}), read normalization (khmer \citep{Pell:2012id}), assembly (Trinity \citep{Haas:2013jq}, SOAPdenovoTrans \citep{Xie:2013wu}) and assembly verificaton (transrate \url{https://github.com/Blahah/transrate} and RSEM-eval \citep{Li:2014er}). \\